| Paper ID | SS-6.5 |
| Paper Title |
LEARNING TO SELECT FOR MIMO RADAR BASED ON HYBRID ANALOG-DIGITAL BEAMFORMING |
| Authors |
Zhaoyi Xu, Rutgers, the State University of New Jersey, United States; Fan Liu, University College London, United Kingdom; Konstantinos Diamantaras, International Hellenic University, Greece; Christos Masouros, University College London, United Kingdom; Athina Petropulu, Rutgers, the State University of New Jersey, United States |
| Session | SS-6: Intelligent Sensing and Communications for Emerging Applications |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Intelligent Sensing and Communications for Emerging Applications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we propose an energy-efficient radar beampattern design framework for a Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a machine learning approach to synthesize the probing beampattern based on a small number of RF chains and antennas. By leveraging a combination of softmax neural networks, the proposed solution is able to achieve a desirable beampattern with high accuracy. |
